Changes In Land Surface Phenology Research Articles

Detection and attribution of the start of the growing season changes in the Northern Hemisphere

Global climate change has led to significant changes in land surface phenology. At present, research on the factors influencing the start of the growing season (SOS) mainly focuses on single factor effects, such as temperature and precipitation, ignoring the combined action of multiple factors. The impact of multiple factors on the spatial and temporal patterns of the SOS in the Northern Hemisphere is not clear, and it is necessary to combine multiple factors to quantify the degrees of influence of different factors on the SOS. Based on the GIMMS3g NDVI dataset, CRU climate data and other factor data, we used geographic detector model, random forest regression model, multiple linear regression, partial correlation analysis and Sen + Mann-Kendall trend analysis to explore the variation of the SOS in the Northern Hemisphere to reveal the main driving factors and impact threshold of 17 influencing factors on the SOS. The results showed that (1) during the past 34 years (1982–2015), the SOS in Europe and Asia mainly showed an advancing trend, whereas the SOS in North America mainly showed a delaying trend. (2) The SOS was mainly controlled by frost frequency, temperature and humidity. Increasing frost frequency inhibited the advancement of the SOS, and increasing temperature and humidity promoted the advancement of the SOS. (3) There were thresholds for the influences of the driving factors on the SOS. Outside the threshold ranges, the response mechanism of the SOS to driving factors changed. The results are important for understanding the response of the SOS to global climate change.

A global increase in tree cover extends the growing season length as observed from satellite records

Plant phenology provides information on the seasonal dynamics of plants, and changes herein are important for understanding the impact of climate change and human management on the biosphere. Land surface phenology is the study of plant phenology across large spatial scales estimated by satellite observations. However, satellite observations (pixels) are often composed of a mixture of vegetation types, like woody vegetation and herbaceous vegetation, having different phenological characteristics. Therefore, any changes in tree cover presumably impact land surface phenology, as trees usually have a different seasonal cycle compared to herbaceous vegetation. On the other hand, changes in land surface phenology are often interpreted as a result of climate change-induced impacts on the photosynthetic activity of vegetation. Therefore, it is important to better understand the role of changes in vegetation cover (here, the proportion between tree and short vegetation cover) in satellite-derived land surface phenology analysis. We studied the impact of changes in tree cover on satellite observed land surface phenology at a global scale over the past three decades. We found an extension of the growing season length in 36.6% of the areas where tree cover increased, whereas only 20.1% of the areas where tree cover decreased showed an increase in growing season length. Furthermore, the ratio between tree cover and short vegetation cover was found to affect changes in the length of the growing season, with the denser tree cover showing a more pronounced extension of the growing season length (especially in boreal forests). These results highlight the importance of changes in tree cover when analyzing the impact of climate change on vegetation phenology. Our study thereby addresses a critical knowledge gap for an improved understanding of changes in land surface phenology during recent decades in the context of climate and human-induced global land cover change.

Characterizing urbanization-induced land surface phenology change from time-series remotely sensed images at fine spatio-temporal scale: A case study in Nanjing, China (2001–2018)

Continuous urban expansion and its influence on land surface phenology (LSP) have been gaining considerable attention due to their impacts on climate change, carbon cycling and human health. Most previous studies have investigated the effects of urbanization on LSP from the regional to global scales by using coarse-resolution remotely sensed images and fixed land cover boundary maps. However, the influences of urbanization on LSP are also important at the local level, particularly in and around rapidly urbanizing cities as LSP is closely linked to the local ecology and people’s health. To analyze the dynamic of urbanization and its effect on LSP at the local scale, this study proposed a novel framework of characterizing LSP with consideration of continuous urban expansion based on all available time-series Landsat and Moderate Resolution Image Spectroradiometer (MODIS) Reflectance and Enhanced Vegetation Index (EVI) composite data. The proposed approach was capable of evaluating LSP impacts of urbanization from three aspects: 1) the phenology difference between dynamic annual urban and rural areas; 2) the different rates of phenology trend between the permanent urban and rural areas; and 3) the phenology shifts in the areas that were urbanized during the period. This approach was applied to Nanjing, China and the experimental results indicated that when compared with the rural areas, the phenology cycle started 0.59 ± 0.58 days earlier (start of season, SOS) and ended 1.65 ± 1.55 days later (end of season, EOS) in urban areas, accordingly leading to increased growing season length (GSL) by 2.77 ± 2.61 days. Besides, the experiments also revealed that 70.81% SOS and 78.79% EOS in the permanent urban-rural areas were provided with delayed phenology trends, along with 80.72% of these regions tending to have a prolonged GSL. Furthermore, the rates of SOS and EOS delay in rural areas were 0.12 ± 0.12 day/year and 0.02 ± 0.02 day/year faster than those in urban areas, and the rate of GSL prolonging in urban regions was 0.04 ± 0.04 day/year faster than that in rural counterparts. For the urbanized regions in Nanjing, after experiencing conversion to the impervious surface, SOS and EOS delayed in 65.77% and 70.33% of the regions, accompanied by extended GSL in 70.83% of the regions. Overall, this research proposed a novel approach of analyzing urbanization implications for LSP at the city scale and demonstrated its priority of taking continuous land cover change into account.

An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes

Monitoring the spatio-temporal development of vegetation is a challenging task in heterogeneous and cloud-prone landscapes. No single satellite sensor has thus far been able to provide consistent time series of high temporal and spatial resolution for such areas. In order to overcome this problem, data fusion algorithms such as the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) have been established and frequently used in recent years to generate high-resolution time series. In order to make it applicable to larger scales and to increase the input data availability especially in cloud-prone areas, an ESTARFM framework was developed in this study introducing several enhancements. An automatic filling of cloud gaps was included in the framework to make best use of available, even partly cloud-covered Landsat images. Furthermore, the ESTARFM algorithm was enhanced to automatically account for regional differences in the heterogeneity of the study area. The generation of time series was automated and the processing speed was accelerated significantly by parallelization. To test the performance of the developed ESTARFM framework, MODIS and Landsat-8 data were fused for generating an 8-day NDVI time series for a study area of approximately 98,000 km2 in West Africa. The results show that the ESTARFM framework can accurately produce high temporal resolution time series (average MAE (mean absolute error) of 0.02 for the dry season and 0.05 for the vegetative season) while keeping the spatial detail in such a heterogeneous, cloud-prone region. The developments introduced within the ESTARFM framework establish the basis for large-scale research on various geoscientific questions related to land degradation, changes in land surface phenology or agriculture.

Extracting Phenological Signals From Multiyear AVHRR NDVI Time Series: Framework for Applying High-Order Annual Splines With Roughness Damping

To better understand how terrestrial vegetative ecosystems respond to climate and/or anthropogenic effects, the scientific community is increasingly interested in developing methods of employing satellite data to track changes in land surface phenology (e.g., timing and rate of green-up, amplitude and duration of growing season, and timing and rate of senescence of plant classes). By increasing the inherent resolution of signal extraction procedures while minimizing the effects of cloud cover and prolonged data gaps, such tools can significantly improve land cover classification and land cover change monitoring on multiple scales. This report describes an intuitive approach for tracking the intra-annual details and interannual variability of multiyear time series, employing a sequence of annual high-order polynomial splines (up to the 14th order), stabilized by minimizing model roughness and weighted to fit the upper data envelope to minimize cloud cover bias. The algorithm is tested using multi-year time series for three very different classes of vegetation-stable agriculture, high elevation montane shrubland, and semi-arid grassland with high interannual variability. The results accurately track both short- and long-term land surface phenology and illustrate a robust potential for extracting temporal and spatial detail from a variety of satellite-based multiyear vegetation signals.

Spectral measures and mixed models as valuable tools for investigating controls on land surface phenology in high arctic Greenland

BackgroundChanges in land surface phenology are of major importance to the understanding of the impact of recent and future climate changes in the Arctic. This paper presents an extensive study from Zackenberg Ecological Research Operations (ZERO) where snow melt, climate and growing season characteristics of six major high arctic vegetation types has been monitored during 1999 to 2005. We investigate the growth dynamics for dry, mesic and wet types using hand held measurements of far red normalised difference vegetation index (NDVI-FR) and generalized additive mixed models (GAMM).ResultsSnow melt and temperature are of major importance for the timing of the maximum growth as well as for the seasonal growth. More than 85% of the variance in timing of the maximum growth is explained by the models and similar for the seasonal growth of mesic and wet vegetation types. We find several non-linear growth responses to the environmental variables.ConclusionWe conclude that the uses of GAMMs are valuable for investigating growth dynamics in the Arctic. Contrary to several other studies in the Arctic we found a significant decreasing trend of the seasonally integrated NDVI-FR (SINDVI) in some vegetation types. This indicates that although greening might occur wide-spread in the Arctic there are variations on the local scale that might influence the regional trends on the longer term.